Uniqueness-Based Resource Allocation for M2M Communications in Narrowband IoT Networks

Machine-type Communications (MTC) are expected to dominate cellular networks traffic by the end of this decade.This makes the radio resource allocation, i.e. scheduling, on these networks, a challenging task. The limited radio resources may not be sufficient for the data transmissions of all the MTC devices (MTCDs) especially in case of massive M2M deployments. Hence, it is essential to allocate radio resources to the MTCDs that send non-redundant or unique data since they are considered to have higher importance. In this paper, we introduce a novel Machine-to-Machine (M2M) resource allocation metric that we term the statistical priority. Statistical priority evaluates the importance of data sent by MTCDs. The importance of a data unit is quantified based on some statistical functions such as, comparison with upper and lower thresholds, difference with earlier data units, and detecting an increasing or decreasing trend when combined with previous data units for prioritizing the allocation of the scarce radio resources to MTCDs sending unique data. Performance evaluation shows that our proposed metric helps achieve effective resource utilization by letting MTCDs send a reduced set of their data that constitute the most important data units that can fully represent the full set of data units.